Assignment 4

Data Visualization – Active Reading

1. Installed tidyverse and imported it.

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.4     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.4     ✓ stringr 1.4.0
## ✓ readr   2.0.2     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(ggplot2)

2. run mpg dataframe

mpg

## # A tibble: 234 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
##  2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
##  3 audi         a4           2    2008     4 manu… f        20    31 p     comp…
##  4 audi         a4           2    2008     4 auto… f        21    30 p     comp…
##  5 audi         a4           2.8  1999     6 auto… f        16    26 p     comp…
##  6 audi         a4           2.8  1999     6 manu… f        18    26 p     comp…
##  7 audi         a4           3.1  2008     6 auto… f        18    27 p     comp…
##  8 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
##  9 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
## 10 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
## # … with 224 more rows

displ, a car’s engine size, in litres.

hwy, a car’s fuel efficiency on the highway, in miles per gallon (mpg). A car with a low fuel efficiency consumes more fuel than a car with a high fuel efficiency when they travel the same distanc

3. Creating a ggplot

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

The plot shows a negative relationship between engine size (displ) and fuel efficiency (hwy). In other words, cars with big engines use more fuel. Does this confirm or refute your hypothesis about fuel efficiency and engine size?

With ggplot2, you begin a plot with the function ggplot(). ggplot() creates a coordinate system that you can add layers to. The first argument of ggplot() is the dataset to use in the graph. So ggplot(data = mpg) creates an empty graph, but it’s not very interesting so I’m not going to show it here.

You complete your graph by adding one or more layers to ggplot(). The function geom_point() adds a layer of points to your plot, which creates a scatterplot. ggplot2 comes with many geom functions that each add a different type of layer to a plot. You’ll learn a whole bunch of them throughout this chapter.

Each geom function in ggplot2 takes a mapping argument. This defines how variables in your dataset are mapped to visual properties. The mapping argument is always paired with aes(), and the x and y arguments of aes() specify which variables to map to the x and y axes. ggplot2 looks for the mapped variables in the data argument, in this case, mpg.

This can be represented in the following template.

ggplot(data = ) + (mapping = aes())

dim(mpg)

## [1] 234  11

?mpg

plot(mpg$hwy, mpg$cyl)

1. I see nothing 2. There are 234 rows and 11 columns 3. drv is the type of drive train where f = front-wheel drive, r = rear wheel drive, 4 = 4wd 4. Its not useful because it is numerical

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = class))

## Warning: Using size for a discrete variable is not advised.

#> Warning: Using size for a discrete variable is not advised.

# Left
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, alpha = class))

## Warning: Using alpha for a discrete variable is not advised.

# Right
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))

## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 7. Consider
## specifying shapes manually if you must have them.

## Warning: Removed 62 rows containing missing values (geom_point).

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

We can use aes to change how our plots look. This refers to aesthetics and ggplot will change it for us.

shape changes shape (only does 6 groups). alpha changes transparency. color changes color

You can also change the color manually.

Common problems are that people will put the + in the wrong place like here: ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl)

One way to add additional variables is with aesthetics. Another way, particularly useful for categorical variables, is to split your plot into facets, subplots that each display one subset of the data.

To facet your plot by a single variable, use facet_wrap(). The first argument of facet_wrap() should be a formula, which you create with ~ followed by a variable name (here “formula” is the name of a data structure in R, not a synonym for “equation”). The variable that you pass to facet_wrap() should be discrete.

We can add a combination of two variable by using facet_grid.

# left
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

# right
ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy, linetype = drv))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

We can also work with geometric objects in our plot to help with visualization. Additionally we can also change the linetype so it is different for each unique value.

ggplot provides over 40 geom plots that we can use to help with data visualization

ggplot(data = mpg) +
  geom_smooth(mapping = aes(x = displ, y = hwy))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = mpg) +
  geom_smooth(mapping = aes(x = displ, y = hwy, group = drv))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = mpg) +
  geom_smooth(
    mapping = aes(x = displ, y = hwy, color = drv),
    show.legend = FALSE
  )

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

We can also display multiple geom on a single plot.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  geom_smooth(mapping = aes(x = displ, y = hwy))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth(data = filter(mpg, class == "subcompact"), se = FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

So we can change mapping such that there are layer of aesthetics (helps us specify data in labels). We can also modify the plot such that our object will only mend to some of the data.

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut))

ggplot(data = diamonds) + 
  stat_count(mapping = aes(x = cut))

A simple barplot where the data is grouped by cut. You can use stat_count and geom_bar interchangably since geom_bar has a default stat_count.

demo <- tribble(
  ~cut,         ~freq,
  "Fair",       1610,
  "Good",       4906,
  "Very Good",  12082,
  "Premium",    13791,
  "Ideal",      21551
)

ggplot(data = demo) +
  geom_bar(mapping = aes(x = cut, y = freq), stat = "identity")

You can also change the stat count.

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, y = stat(prop), group = 1))

You might want to override the default mapping from transformed variables to aesthetics. For example, you might want to display a bar chart of proportion, rather than count:

ggplot(data = diamonds) + 
  stat_summary(
    mapping = aes(x = cut, y = depth),
    fun.min = min,
    fun.max = max,
    fun = median
  )

we can do it for a transformation on statisitcs aswell where we take a stat summary and take the specific val of each category.

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, colour = cut))

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = cut))

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity))

ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + 
  geom_bar(alpha = 1/5, position = "identity")

ggplot(data = diamonds, mapping = aes(x = cut, colour = clarity)) + 
  geom_bar(fill = NA, position = "identity")

Now, we can change the position such that they can either overlap on each other and see something special about the data.

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "fill")

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge")

We can use things like position to see proportion to data or dodge to overlap them.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), position = "jitter")

We can change our data such that their isn’t overplotting such that the arrangement is now easier to see.

The template for all this is:

ggplot(data = ) + ( mapping = aes(), stat = , position = ) + +

“To see this, let’s add position adjustments, stats, coordinate systems, and faceting to our code template:”

Graphics for communication

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  labs(title = "Fuel efficiency generally decreases with engine size")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  labs(
    title = "Fuel efficiency generally decreases with engine size",
    subtitle = "Two seaters (sports cars) are an exception because of their light weight",
    caption = "Data from fueleconomy.gov"
  )

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
  labs(
    x = "Engine displacement (L)",
    y = "Highway fuel economy (mpg)",
    colour = "Car type"
  )

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

df <- tibble(
  x = runif(10),
  y = runif(10)
)
ggplot(df, aes(x, y)) +
  geom_point() +
  labs(
    x = quote(sum(x[i] ^ 2, i == 1, n)),
    y = quote(alpha + beta + frac(delta, theta))
  )

We can put labels in places like the title. You can also add them as subtitles, captions, x, and y. Labels can even be written as mathematical equations.

You can take it a step further and make annotations for specific points

best_in_class <- mpg %>%
  group_by(class) %>%
  filter(row_number(desc(hwy)) == 1)

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_text(aes(label = model), data = best_in_class)

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_label(aes(label = model), data = best_in_class, nudge_y = 2, alpha = 0.5)

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_point(size = 3, shape = 1, data = best_in_class) +
  ggrepel::geom_label_repel(aes(label = model), data = best_in_class)

In this graph, we label the cars that are the best in their class in terms of mpg.

The label is hard to read from one another, so we can modify it such that it is easier to see. Additionally, if you use ggrepel then you can make it so the labels are not right on top of each other.

class_avg <- mpg %>%
  group_by(class) %>%
  summarise(
    displ = median(displ),
    hwy = median(hwy)
  )
#> `summarise()` ungrouping output (override with `.groups` argument)

ggplot(mpg, aes(displ, hwy, colour = class)) +
  ggrepel::geom_label_repel(aes(label = class),
    data = class_avg,
    size = 6,
    label.size = 0,
    segment.color = NA
  ) +
  geom_point() +
  theme(legend.position = "none")

label <- mpg %>%
  summarise(
    displ = max(displ),
    hwy = max(hwy),
    label = "Increasing engine size is \nrelated to decreasing fuel economy."
  )

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_text(aes(label = label), data = label, vjust = "top", hjust = "right")

label <- tibble(
  displ = Inf,
  hwy = Inf,
  label = "Increasing engine size is \nrelated to decreasing fuel economy."
)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_text(aes(label = label), data = label, vjust = "top", hjust = "right")

We can turn the legend off and then put annotations near the colors such that they represent what each car type is. Additionally, you can just add a single label to the plot and then write what you want.

If you want to place text exactly where the borders are then you can use INF(which can be positive or negative).

There are nine different possibilities for locations that labels can go in (NESW).

Use geom_hline() and geom_vline() to add reference lines. I often make them thick (size = 2) and white (colour = white), and draw them underneath the primary data layer. That makes them easy to see, without drawing attention away from the data.

Use geom_rect() to draw a rectangle around points of interest. The boundaries of the rectangle are defined by aesthetics xmin, xmax, ymin, ymax.

Use geom_segment() with the arrow argument to draw attention to a point with an arrow. Use aesthetics x and y to define the starting location, and xend and yend to define the end location.

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class))

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  scale_x_continuous() +
  scale_y_continuous() +
  scale_colour_discrete()

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  scale_y_continuous(breaks = seq(15, 40, by = 5))

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  scale_x_continuous(labels = NULL) +
  scale_y_continuous(labels = NULL)

Another way we can change how the plot looks for communication is by adjusting the scales of the plot. ggplot will automatically add the scales behind the scenes but you can manually change these how you would like.

There are two primary arguments that affect the appearance of the ticks on the axes and the keys on the legend: breaks and labels. Breaks controls the position of the ticks, or the values associated with the keys. Labels controls the text label associated with each tick/key. The most common use of breaks is to override the default choice:

You can also set the values within these scales (labels) to null so it doesn’t share the absolute value of the data but you can still see some sort of trend that is associated with it.

presidential %>%
  mutate(id = 33 + row_number()) %>%
  ggplot(aes(start, id)) +
    geom_point() +
    geom_segment(aes(xend = end, yend = id)) +
    scale_x_date(NULL, breaks = presidential$start, date_labels = "'%y")

Collectively axes and legends are called guides. Axes are used for x and y aesthetics; legends are used for everything else.

You can scale the dates such that in this plot it shows us when the president start and ended their term.

Note that the specification of breaks and labels for date and datetime scales is a little different:

date_labels takes a format specification, in the same form as parse_datetime().

date_breaks (not shown here), takes a string like “2 days” or “1 month”.

base <- ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class))

base + theme(legend.position = "left")

base + theme(legend.position = "top")

base + theme(legend.position = "bottom")

base + theme(legend.position = "right") # the default

You can edit the legend by changing its position on the graph, or you can set the legend position to none such that it won’t be anywhere.

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
  theme(legend.position = "bottom") +
  guides(colour = guide_legend(nrow = 1, override.aes = list(size = 4)))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

To control the display of individual legends, use guides() along with guide_legend() or guide_colourbar(). The following example shows two important settings: controlling the number of rows the legend uses with nrow, and overriding one of the aesthetics to make the points bigger. This is particularly useful if you have used a low alpha to display many points on a plot.

ggplot(diamonds, aes(carat, price)) +
  geom_bin2d()

ggplot(diamonds, aes(log10(carat), log10(price))) +
  geom_bin2d()

ggplot(diamonds, aes(carat, price)) +
  geom_bin2d() + 
  scale_x_log10() + 
  scale_y_log10()

We can also change the scale of our plot. However it could be disadvantageous since the labels are now the transformed version of the data. This last example is the same but the axes are labeled on the original data scale.

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = drv))

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = drv)) +
  scale_colour_brewer(palette = "Set1")

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = drv, shape = drv)) +
  scale_colour_brewer(palette = "Set1")

You can also change the color or these points to help people who may have colorblindness. We can also edit the shape of these points in the graph.

The ColorBrewer scales are documented online at http://colorbrewer2.org/ and made available in R via the RColorBrewer package, by Erich Neuwirth.

presidential %>%
  mutate(id = 33 + row_number()) %>%
  ggplot(aes(start, id, colour = party)) +
    geom_point() +
    geom_segment(aes(xend = end, yend = id)) +
    scale_colour_manual(values = c(Republican = "red", Democratic = "blue"))

df <- tibble(
  x = rnorm(10000),
  y = rnorm(10000)
)
ggplot(df, aes(x, y)) +
  geom_hex() +
  coord_fixed()

ggplot(df, aes(x, y)) +
  geom_hex() +
  viridis::scale_fill_viridis() +
  coord_fixed()

You can also set the color manually.

For continuous colour, you can use the built-in scale_colour_gradient() or scale_fill_gradient(). If you have a diverging scale, you can use scale_colour_gradient2(). That allows you to give, for example, positive and negative values different colours. That’s sometimes also useful if you want to distinguish points above or below the mean.

Another option is scale_colour_viridis() provided by the viridis package. It’s a continuous analog of the categorical ColorBrewer scales. The designers, Nathaniel Smith and Stéfan van der Walt, carefully tailored a continuous colour scheme that has good perceptual properties. Here’s an example from the viridis vignette.

ggplot(mpg, mapping = aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth() +
  coord_cartesian(xlim = c(5, 7), ylim = c(10, 30))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

mpg %>%
  filter(displ >= 5, displ <= 7, hwy >= 10, hwy <= 30) %>%
  ggplot(aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

suv <- mpg %>% filter(class == "suv")
compact <- mpg %>% filter(class == "compact")

ggplot(suv, aes(displ, hwy, colour = drv)) +
  geom_point()

ggplot(compact, aes(displ, hwy, colour = drv)) +
  geom_point()

x_scale <- scale_x_continuous(limits = range(mpg$displ))
y_scale <- scale_y_continuous(limits = range(mpg$hwy))
col_scale <- scale_colour_discrete(limits = unique(mpg$drv))

ggplot(suv, aes(displ, hwy, colour = drv)) +
  geom_point() +
  x_scale +
  y_scale +
  col_scale

ggplot(compact, aes(displ, hwy, colour = drv)) +
  geom_point() +
  x_scale +
  y_scale +
  col_scale

You can also set the limits on individual scales. Reducing the limits is basically equivalent to subsetting the data. It is generally more useful if you want expand the limits, for example, to match scales across different plots. For example, if we extract two classes of cars and plot them separately, it’s difficult to compare the plots because all three scales (the x-axis, the y-axis, and the colour aesthetic) have different ranges.

One way to overcome this problem is to share scales across multiple plots, training the scales with the limits of the full data. In this particular case, you could have simply used faceting, but this technique is useful more generally, if for instance, you want spread plots over multiple pages of a report.

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  theme_bw()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

We can set themes for our plot as well and there are 8 built in ones for gg.plot.

ggplot(mpg, aes(displ, hwy)) + geom_point()

ggsave("my-plot.pdf")

## Saving 7 x 5 in image

#> Saving 7 x 4.33 in image

There are two main ways to get your plots out of R and into your final write-up: ggsave() and knitr. ggsave() will save the most recent plot to disk.

But if you don’t specify the width and the height it will just take from what the current plotting has. So for reproducability it is important to do these.

There are five main options that control figure sizing: fig.width, fig.height, fig.asp, out.width and out.height

They said they only use three out of the five :

“I find it most aesthetically pleasing for plots to have a consistent width. To enforce this, I set fig.width = 6 (6”) and fig.asp = 0.618 (the golden ratio) in the defaults. Then in individual chunks, I only adjust fig.asp.

I control the output size with out.width and set it to a percentage of the line width. I default to out.width = “70%” and fig.align = “center”. That give plots room to breathe, without taking up too much space.

To put multiple plots in a single row I set the out.width to 50% for two plots, 33% for 3 plots, or 25% to 4 plots, and set fig.align = “default”. Depending on what I’m trying to illustrate (e.g. show data or show plot variations), I’ll also tweak fig.width, as discussed below."

If you find that you’re having to squint to read the text in your plot, you need to tweak fig.width. If fig.width is larger than the size the figure is rendered in the final doc, the text will be too small; if fig.width is smaller, the text will be too big. You’ll often need to do a little experimentation to figure out the right ratio between the fig.width and the eventual width in your document.

If you want to make sure the font size is consistent across all your figures, whenever you set out.width, you’ll also need to adjust fig.width to maintain the same ratio with your default out.width.

He said the following: “I recommend setting fig.show =”hold" so that plots are shown after the code. This has the pleasant side effect of forcing you to break up large blocks of code with their explanations.

To add a caption to the plot, use fig.cap. In R Markdown this will change the figure from inline to “floating”."